Three easy ways for separating nonlinear mixtures?
Signal Processing - Special issue on independent components analysis and beyond
Blind separation of convolutive image mixtures
Neurocomputing
EURASIP Journal on Advances in Signal Processing
Blind maximum likelihood identification of Hammerstein systems
Automatica (Journal of IFAC)
Maximum likelihood linear programming data fusion for speaker recognition
Speech Communication
Initialisation of Nonlinearities for PNL and Wiener systems Inversion
IWANN '03 Proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks: Part II: Artificial Neural Nets Problem Solving Methods
Blind maximum-likelihood identification of wiener systems
IEEE Transactions on Signal Processing
An evolutionary approach for blind inversion of wiener systems
ICA'07 Proceedings of the 7th international conference on Independent component analysis and signal separation
A simple approximation for fast nonlinear deconvolution
NOLISP'11 Proceedings of the 5th international conference on Advances in nonlinear speech processing
A canonical genetic algorithm for blind inversion of linear channels
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
Blind channel deconvolution of real world signals using source separation techniques
NOLISP'05 Proceedings of the 3rd international conference on Non-Linear Analyses and Algorithms for Speech Processing
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An efficient procedure for the blind inversion of a nonlinear Wiener system is proposed. We show that the problem can be expressed as a problem of blind source separation in nonlinear mixtures for which a solution has been previously proposed. Based on a quasi-nonparametric relative gradient descent, the proposed algorithm can perform efficiently even in the presence of hard distortions